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The uncertain nature of wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple wind farms are few. Although the traditional methods for the single wind farm can still be used, they have the limitations in capturing the spatial correlations among...
Accurate automatic optimization heuristics are necessary for dealing with thecomplexity and diversity of modern hardware and software. Machine learning is aproven technique for learning such heuristics, but its success is bound by thequality of the features used. These features must be hand crafted by developersthrough a combination of expert domain knowledge and trial and error. This makesthe quality...
Energy usage in data centres continues to be a major and growing concern as an increasing number of everyday services depend on these facilities. Research in this area has examined topics including power smoothing using batteries and deep learning to control cooling systems, in addition to optimisation techniques for the software running inside data centres. We present a novel real-time power-cycling...
Although solar power is one of the most widely used renewable energy sources, it is highly variable and needs accurate forecasting for its large-scale integration into the electricity grid. We propose WPP, a Weather type Pair Pattern approach, for directly and simultaneously predicting the solar power output for the next day at half-hourly intervals. WPP firstly partitions the days from the training...
We consider the task of predicting the half-hourly solar PhotoVoltaic (PV) power output for the next day from three sources: previous solar power, previous weather data and weather forecasts. We propose DWkNN, a data source weighed nearest neighbor method that considers the importance of the different data sources and learns the best weights for them. We evaluate its performance using Australian PV...
Predictive modeling using machine learning is an effective method for building compiler heuristics, but there is a shortage of benchmarks. Typical machine learning experiments outside of the compilation field train over thousands or millions of examples. In machine learning for compilers, however, there are typically only a few dozen common benchmarks available. This limits the quality of learned...
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build...
Building effective optimization heuristics is a challenging task which often takes developers several months if not years to complete. Predictive modelling has recently emerged as a promising solution, automatically constructing heuristics from training data, however, obtaining this data can take months per platform. This is becoming an ever more critical problem as the pace of change in architecture...
The Domain Name System (DNS) resolution is usually served by multiple geographically distant servers. In planning and optimizing DNS server number, placement, and capacity, it is important to predict server load distribution given some knowledge about the network locations and query rates of active caching resolvers. This poster proposes an analytical model for predicting the DNS server load distribution...
This paper employed the carbon dynamic model and IPCC cement model to estimate the amount of carbon emitted by energy and cement, and used CO2FIX model to predict the forest carbon sinks in Beijing, Tianjin and Hebei Province from 2009 to 2050, then, this paper analyzed the influential factors and the contribution of forest carbon sinks to carbon emissions reduction. The main conclusions are as follows:...
This article did some simulation in allusion to Shanghai's carbon emissions before 2050 and discussed research methods of urban carbon emissions trends. In allusion to urban problems, this paper used the Logistic curve to approach urban population growth rate and employed Nonlinear Economic Dynamics to predict Shanghai's economic growth rate before 2050. Adopting the optimal growth model proposed...
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